Capsule endoscopy has recently emerged as a valuable imaging technology for the gastrointestinal (GI) tract, especially the small bowel and the esophagus. With this technology, it has become possible to directly evaluate the gut mucosa of patients with a variety of conditions, such as obscure gastrointestinal bleeding, celiac disease and Crohn's disease. Although the use of capsule endoscopy is gaining rapidly, the evaluation of capsule endoscopic imagery presents numerous practical challenges. In a typical case, the capsule acquires 50,000 or more images over an eight-hour period. The quality of these images is highly variable due to the uncontrolled motion of the capsule itself as it moves through the GI tract, the complexity of the structures being imaged, and inherent limitations of the imager itself. In practice, relatively few (often less than 100) of these images contain significant diagnostic content. As a result, it is challenging to create an effective, repeatable means for evaluating capsule endoscopic sequences. The goal of this project is create a tool for semi-automated, objective, quantitative assessment of pathologic findings in capsule endoscopic data. The clinical focus will be on quantitative assessment of lesions that appear in Crohn's disease of the small bowel. The technical approach to this problem will make use of statistical learning methods to create algorithms that perform lesion classification and assessment in a manner consistent with a trained expert. The underlying hypothesis of this project is that appropriately constructed algorithms will be able to perform assessment of lesions appearing in capsule endoscopic images with a level of consistency comparable to human observers. In proving this hypothesis, the proposed project will pursue the following three specific aims: [unreadable] [unreadable] Aim 1: Data acquisition. To develop a substantial database of images of intestinal lesions together with an expert assessment of several attributes indicative of lesion severity. [unreadable] [unreadable] Aim 2: Tissue classification and image enhancement. To develop algorithms for low-level classification of tissue type from image content using statistical learning techniques, and to create algorithms for registering multiple partial views of a lesion to create more complete views. [unreadable] [unreadable] Aim 3: Automated Lesion Assessment. To apply and validate statistical learning methods that can assess the images produced by Aim 2 in a manner consistent with the expert assessments compiled in Aim 1. [unreadable] [unreadable] The focus of this R21 is on the development of tools that have proven efficacy on a representative corpus of data. This will set the stage for subsequent technological developments leading toward the automated detection of lesions, and subsequent clinical studies addressing the development of quantitative measures for Crohn's disease severity in a more substantial clinical setting. [unreadable] [unreadable] [unreadable]